Experiment with Random Difficulty (results based on the bet)
## IDjoueur nom_du_joueur heure_connexion_joueur nom_du_jeu
## 1: 05ezh8lfl Sandrine Bruneau 12_20_2016_15h14m33s Logique2
## 2: 05ezh8lfl Sandrine Bruneau 12_20_2016_15h14m33s Logique2
## 3: 05ezh8lfl Sandrine Bruneau 12_20_2016_15h14m33s Logique2
## 4: 05ezh8lfl Sandrine Bruneau 12_20_2016_15h14m33s Logique2
## 5: 05ezh8lfl Sandrine Bruneau 12_20_2016_15h14m33s Logique2
## ---
## 5738: zv35u39vc Nadège BELLEC 12_20_2016_12h33m13s Motrice
## 5739: zv35u39vc Nadège BELLEC 12_20_2016_12h33m13s Motrice
## 5740: zv35u39vc Nadège BELLEC 12_20_2016_12h33m13s Motrice
## 5741: zv35u39vc Nadège BELLEC 12_20_2016_12h33m13s Motrice
## 5742: zv35u39vc Nadège BELLEC 12_20_2016_12h33m13s Motrice
## modeTest mise_first_1 action_de_jeu duree_tour_ms mise confiance
## 1: 1 1 1 34341 7 100
## 2: 1 1 2 24837 4 50
## 3: 1 1 3 31080 5 90
## 4: 0 1 1 20435 7 100
## 5: 0 1 2 43967 3 50
## ---
## 5738: 0 0 26 6363 7 100
## 5739: 0 0 27 6401 1 10
## 5740: 0 0 28 7363 2 30
## 5741: 0 0 29 6833 2 30
## 5742: 0 0 30 8730 1 20
## difficulty gameDiff near_miss moutons_sauves moutons_tues score
## 1: 0.00 1.00 0 7 0 7
## 2: 0.10 2.00 0 11 0 11
## 3: 0.20 3.00 0 11 5 6
## 4: 0.00 1.00 0 7 0 7
## 5: 0.41 5.00 0 10 0 10
## ---
## 5738: 0.20 3.60 17 60 59 1
## 5739: 0.87 8.96 -28 60 60 0
## 5740: 0.36 4.88 -10 62 60 2
## 5741: 0.55 6.40 -16 62 62 0
## 5742: 0.93 9.44 52 62 63 -1
## gagnant horodateur prenomNom age sexe
## 1: 1 12/20/2016 15:20:04 Sandrine Bruneau 46 1
## 2: 1 12/20/2016 15:20:04 Sandrine Bruneau 46 1
## 3: 0 12/20/2016 15:20:04 Sandrine Bruneau 46 1
## 4: 1 12/20/2016 15:20:04 Sandrine Bruneau 46 1
## 5: 1 12/20/2016 15:20:04 Sandrine Bruneau 46 1
## ---
## 5738: 0 12/20/2016 12:37:14 Nad<U+008A>ge BELLEC 38 1
## 5739: 0 12/20/2016 12:37:14 Nad<U+008A>ge BELLEC 38 1
## 5740: 1 12/20/2016 12:37:14 Nad<U+008A>ge BELLEC 38 1
## 5741: 0 12/20/2016 12:37:14 Nad<U+008A>ge BELLEC 38 1
## 5742: 0 12/20/2016 12:37:14 Nad<U+008A>ge BELLEC 38 1
## langueMaternelle niveauEtude
## 1: 1 7
## 2: 1 7
## 3: 1 7
## 4: 1 7
## 5: 1 7
## ---
## 5738: 1 4
## 5739: 1 4
## 5740: 1 4
## 5741: 1 4
## 5742: 1 4
## jeuxFav autoEffJoueur1
## 1: pacman_ NA
## 2: pacman_ NA
## 3: pacman_ NA
## 4: pacman_ NA
## 5: pacman_ NA
## ---
## 5738: TRI PEAK SOLITAIRE - BEST FRIEND - SUR ANDROID NA
## 5739: TRI PEAK SOLITAIRE - BEST FRIEND - SUR ANDROID NA
## 5740: TRI PEAK SOLITAIRE - BEST FRIEND - SUR ANDROID NA
## 5741: TRI PEAK SOLITAIRE - BEST FRIEND - SUR ANDROID NA
## 5742: TRI PEAK SOLITAIRE - BEST FRIEND - SUR ANDROID NA
## autoEffJoueur2 autoEffJoueur3 autoEffJoueur4 autoEffJoueur5
## 1: NA NA NA NA
## 2: NA NA NA NA
## 3: NA NA NA NA
## 4: NA NA NA NA
## 5: NA NA NA NA
## ---
## 5738: NA NA NA NA
## 5739: NA NA NA NA
## 5740: NA NA NA NA
## 5741: NA NA NA NA
## 5742: NA NA NA NA
## autoEffJoueur6 autoEffJoueur7 autoEffJoueur8 autoEffJoueur9
## 1: NA NA NA NA
## 2: NA NA NA NA
## 3: NA NA NA NA
## 4: NA NA NA NA
## 5: NA NA NA NA
## ---
## 5738: NA NA NA NA
## 5739: NA NA NA NA
## 5740: NA NA NA NA
## 5741: NA NA NA NA
## 5742: NA NA NA NA
## autoEffJoueur10 loterie1 loterie2 loterie3 loterie4 loterie5
## 1: NA 1 1 1 1 0
## 2: NA 1 1 1 1 0
## 3: NA 1 1 1 1 0
## 4: NA 1 1 1 1 0
## 5: NA 1 1 1 1 0
## ---
## 5738: NA 1 1 0 0 1
## 5739: NA 1 1 0 0 1
## 5740: NA 1 1 0 0 1
## 5741: NA 1 1 0 0 1
## 5742: NA 1 1 0 0 1
## loterie6 loterie7 loterie8 loterie9 loterie10 profilJoueur8
## 1: 0 1 1 1 1 0
## 2: 0 1 1 1 1 0
## 3: 0 1 1 1 1 0
## 4: 0 1 1 1 1 0
## 5: 0 1 1 1 1 0
## ---
## 5738: 1 1 1 1 1 0
## 5739: 1 1 1 1 1 0
## 5740: 1 1 1 1 1 0
## 5741: 1 1 1 1 1 0
## 5742: 1 1 1 1 1 0
## play.video.games play.board.games play.money.games self.eff
## 1: 0.4 0.2 0.8 NA
## 2: 0.4 0.2 0.8 NA
## 3: 0.4 0.2 0.8 NA
## 4: 0.4 0.2 0.8 NA
## 5: 0.4 0.2 0.8 NA
## ---
## 5738: 1.0 0.4 0.4 NA
## 5739: 1.0 0.4 0.4 NA
## 5740: 1.0 0.4 0.4 NA
## 5741: 1.0 0.4 0.4 NA
## 5742: 1.0 0.4 0.4 NARemoving Outliers
## [1] "Outliers : 3qq8dp8jk, 79pn8m6v8, e58u3sinl, hudayxdge, w2x28nknu"
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Outliers : "## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Outliers : "## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur## [1] "Total number of outliers: 5"## [1] "Total number of outliers motor task: 1"## [1] "Total number of outliers perceptive task: 1"## [1] "Total number of outliers logical task: 3"Modeling objective difficulty for motor task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 2268.1 2290.2 -1130.0 2260.1 1877
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9846 -0.7313 0.2308 0.7546 2.8895
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.5178 0.7196
## Number of obs: 1881, groups: IDjoueur, 57
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.1555 0.1548 -7.464 8.42e-14 ***
## difficulty 3.0512 0.2019 15.113 < 2e-16 ***
## timeNorm -0.3871 0.1728 -2.241 0.0251 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.488
## timeNorm -0.430 -0.167## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 0 1881 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-0.9973563
## 1st Qu.:-0.4243437
## Median :-0.1362009
## Mean :-0.0003041
## 3rd Qu.: 0.3781255
## Max. : 1.6570924
## [1] "Intercept: -1.16 8.4e-14 ***"
## [1] "Difficulty: 3.05 1.3e-51 ***"
## [1] "Time: -0.387 0.025 *"
## [1] "R2 fixed: 0.16"
## [1] "R2 mixed: 0.28"
## [1] "Cross Val: 0.69"
## [1] "AIC: 2300"## 0% 25% 50% 75% 100%
## -1.6570924 -0.3781255 0.1362009 0.4243437 0.9973563
## 0% 25% 50% 75% 100%
## -1.6570924 -0.3781255 0.1362009 0.4243437 0.9973563
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Modeling objective difficulty for sensory task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1535.4 1557.4 -763.7 1527.4 1811
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.2914 -0.4479 0.1164 0.3982 4.7670
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.5772 0.7598
## Number of obs: 1815, groups: IDjoueur, 55
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.2311 0.1826 -12.217 < 2e-16 ***
## difficulty 7.0302 0.3250 21.631 < 2e-16 ***
## timeNorm -1.0832 0.2369 -4.572 4.84e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.458
## timeNorm -0.385 -0.358## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 0 0 1815
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.527169
## 1st Qu.:-0.388916
## Median :-0.005975
## Mean : 0.002363
## 3rd Qu.: 0.374680
## Max. : 1.350107
## [1] "Intercept: -2.23 2.5e-34 ***"
## [1] "Difficulty: 7.03 9.1e-104 ***"
## [1] "Time: -1.08 4.8e-06 ***"
## [1] "R2 fixed: 0.55"
## [1] "R2 mixed: 0.62"
## [1] "Cross Val: 0.81"
## [1] "AIC: 1500"## 0% 25% 50% 75% 100%
## -1.350106912 -0.374679910 0.005974618 0.388915836 1.527169116
## 0% 25% 50% 75% 100%
## -1.350106912 -0.374679910 0.005974618 0.388915836 1.527169116
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Modeling objective difficulty for logical task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1816.7 1838.8 -904.3 1808.7 1877
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.7618 -0.5239 -0.1972 0.5160 5.0573
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 1.067 1.033
## Number of obs: 1881, groups: IDjoueur, 57
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.6536 0.1924 -8.596 <2e-16 ***
## difficulty 5.4305 0.2647 20.515 <2e-16 ***
## timeNorm -2.0774 0.2224 -9.340 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.388
## timeNorm -0.276 -0.437## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 1881 0 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.492039
## 1st Qu.:-0.741161
## Median :-0.213560
## Mean : 0.004668
## 3rd Qu.: 0.599760
## Max. : 2.373359
## [1] "Intercept: -1.65 8.2e-18 ***"
## [1] "Difficulty: 5.43 1.6e-93 ***"
## [1] "Time: -2.08 9.6e-21 ***"
## [1] "R2 fixed: 0.38"
## [1] "R2 mixed: 0.53"
## [1] "Cross Val: 0.79"
## [1] "AIC: 1800"## 0% 25% 50% 75% 100%
## -2.3733594 -0.5997602 0.2135598 0.7411607 1.4920388
## 0% 25% 50% 75% 100%
## -2.3733594 -0.5997602 0.2135598 0.7411607 1.4920388
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Influence of Player Profiles
Player profiles
Influence of Player Profiles
Objective level and player profile
Playing video games in general and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.0832, p-value = 0.2787
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1121498
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.27984, p-value = 0.7796
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.02959975
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.18429, p-value = 0.8538
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.01913758Playing board games in general and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.92279, p-value = 0.3561
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.09432639
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.40055, p-value = 0.6887
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.04164333
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.83074, p-value = 0.4061
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.08524489Self efficacy and level for each task
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 29 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.17852, p-value = 0.8583
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.02429648## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 27 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.5588, p-value = 0.0105
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.3482495
##
## [1] "self.eff.on.level.s 0.35 0.011 *"## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 28 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.77294, p-value = 0.4396
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1034345Risk aversion and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.2531, p-value = 0.2102
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1232133
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.9255, p-value = 0.05417
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1918732
##
## [1] "risk.av.on.level.s 0.19 0.054 ."
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.0617, p-value = 0.2884
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1042971Age and level for each task
## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.0129, p-value = 0.3111
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.09643322## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.0949, p-value = 0.03618
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2036664
##
## [1] "age.on.level.s 0.2 0.036 *"## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.2495, p-value = 0.2115
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1192254Sex and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.3361, p-value = 0.01949
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.257113
##
## [1] "sexe.on.level.m -0.26 0.019 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0, p-value = 1
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.18884, p-value = 0.8502
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.02078441
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 223, p-value = 0.01897
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.85282846 -0.09534056
## sample estimates:
## difference in location
## -0.5051082
##
## [1] "sexe.on.level.m.2 -0.51 0.019 * mean(A): 0.16 mean(B): -0.32"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 333, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.3670949 0.4731302
## sample estimates:
## difference in location
## -0.0009246191
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 340, p-value = 0.8583
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.7335260 0.5047401
## sample estimates:
## difference in location
## -0.02802612Subjective difficulty and play habits
Playing video game in general and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.62185, p-value = 0.534
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.03720939Playing board game in general and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -3.4464, p-value = 0.0005681
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2033235
##
## [1] "pbg.on.error -0.2 0.00057 ***"In game level and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.44873, p-value = 0.6536
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.02338143
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.23405, p-value = 0.8149
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.02130326
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.094374, p-value = 0.9248
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.008754209
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.45433, p-value = 0.6496
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.04135338Sex and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 4.1645, p-value = 3.12e-05
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2646112
##
## [1] "sexe.on.error 0.26 3.1e-05 ***"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.3699, p-value = 0.01779
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2608393
##
## [1] "sexe.on.error.m 0.26 0.018 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.565, p-value = 0.01032
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2875846
##
## [1] "sexe.on.error.s 0.29 0.01 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.2318, p-value = 0.02563
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2456339
##
## [1] "sexe.on.error.l 0.25 0.026 *"
##
## Wilcoxon rank sum test with continuity correction
##
## data: B and A
## W = 4376, p-value = 3.143e-05
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.04977679 0.13237866
## sample estimates:
## difference in location
## 0.09299933
##
## [1] "sexe.on.error.2 0.093 3.1e-05 *** mean(A): -0.093 mean(B): 0.001"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 501, p-value = 0.01724
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.01355287 0.15331497
## sample estimates:
## difference in location
## 0.09290042
##
## [1] "sexe.on.error.m.2 0.093 0.017 * mean(A): -0.085 mean(B): 0.0073"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 476, p-value = 0.009655
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.02092227 0.15744127
## sample estimates:
## difference in location
## 0.09796631
##
## [1] "sexe.on.error.s.2 0.098 0.0097 ** mean(A): -0.1 mean(B): -0.0014"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 481, p-value = 0.02523
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.009389481 0.150561466
## sample estimates:
## difference in location
## 0.09060751
##
## [1] "sexe.on.error.l.2 0.091 0.025 * mean(A): -0.091 mean(B): -0.0033"Risk aversion and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.60676, p-value = 0.544
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.03431688
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.12035, p-value = 0.9042
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.01183404
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.11152, p-value = 0.9112
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.01111235
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.79275, p-value = 0.4279
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.07787518Self efficacy and subjective difficulty error
## Warning: Removed 84 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.9644, p-value = 0.003033
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2277125
##
## [1] "self.eff.on.error -0.23 0.003 **"## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 29 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.7653, p-value = 0.07751
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2402652
##
## [1] "self.eff.on.error -0.24 0.078 ."## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 27 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.6463, p-value = 0.09969
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2240675
##
## [1] "self.eff.on.error -0.22 0.1 :("## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 28 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.6401, p-value = 0.101
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2194829OLD!! We investigate the link between player’s reported game habits, feeling of self efficacy, risk aversion and player’s behavior in the different games. Feeling of self efficacy shows a small link with performance on motor task (Kendal \(\tau\)=0.26, p<0.01) and logical task (Kendal \(\tau\)=0.17, p=0.053). Aversion to risk shows a small link with performance on sensory (Kendal \(\tau\)=0.29, p<0.001) and logical task (Kendal \(\tau\)=0.27 p<0.01). In this experiment, female players tend to have a lower performance on motor (Kendal \(\tau\)=-0.4, p<0.001) and logical tasks (Kendal \(\tau\)=-0.25, p<0.01). Player’s sex is also slightly related to the error between subjective and objective difficulty (Kendal \(\tau\)=-0.19, p=0.053) i.e. compared to male players, female players tend to underestimate logical task difficulty.
Influence of Objective difficulty on Subjective Difficulty
All tasks
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0096 47 0.64 :(
## 2: 0.09375 0.0440 54 0.052 .
## 3: 0.15625 0.0045 58 0.91 :(
## 4: 0.21875 0.0260 58 0.27 :(
## 5: 0.28125 0.0044 57 0.98 :(
## 6: 0.34375 -0.0400 58 0.25 :(
## 7: 0.40625 -0.0400 58 0.23 :(
## 8: 0.46875 -0.0045 58 0.94 :(
## 9: 0.53125 -0.0190 58 0.54 :(
## 10: 0.59375 -0.0420 58 0.18 :(
## 11: 0.65625 -0.0370 58 0.31 :(
## 12: 0.71875 -0.1100 58 1.9e-05 ***
## 13: 0.78125 -0.1400 58 7.4e-08 ***
## 14: 0.84375 -0.2100 58 1.7e-09 ***
## 15: 0.90625 -0.1900 57 5e-11 ***
## 16: 0.96875 -0.1800 55 1.1e-10 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 47 0.64 :(
## 2: 54 0.052 .
## 3: 58 0.91 :(
## 4: 58 0.27 :(
## 5: 57 0.98 :(
## 6: 58 0.25 :(
## 7: 58 0.23 :(
## 8: 58 0.94 :(
## 9: 58 0.54 :(
## 10: 58 0.18 :(
## 11: 58 0.31 :(
## 12: 58 1.9e-05 ***
## 13: 58 7.4e-08 ***
## 14: 58 1.7e-09 ***
## 15: 57 5e-11 ***
## 16: 55 1.1e-10 ***
## [1] 56.8
## [1] 2.86
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0093 28 0.64 :(
## 2: 0.09375 -0.0220 31 0.41 :(
## 3: 0.15625 -0.0970 40 0.031 *
## 4: 0.21875 -0.0400 38 0.08 .
## 5: 0.28125 -0.0360 34 0.38 :(
## 6: 0.34375 -0.0580 38 0.17 :(
## 7: 0.40625 -0.0610 34 0.27 :(
## 8: 0.46875 0.0220 36 0.64 :(
## 9: 0.53125 0.0400 36 0.38 :(
## 10: 0.59375 -0.0580 38 0.22 :(
## 11: 0.65625 -0.0850 35 0.11 :(
## 12: 0.71875 -0.1500 35 5e-04 ***
## 13: 0.78125 -0.1500 33 0.00081 ***
## 14: 0.84375 -0.2700 25 0.00014 ***
## 15: 0.90625 -0.1900 23 2.7e-05 ***
## 16: 0.96875 -0.1500 19 0.00011 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 28 0.64 :(
## 2: 31 0.41 :(
## 3: 40 0.031 *
## 4: 38 0.08 .
## 5: 34 0.38 :(
## 6: 38 0.17 :(
## 7: 34 0.27 :(
## 8: 36 0.64 :(
## 9: 36 0.38 :(
## 10: 38 0.22 :(
## 11: 35 0.11 :(
## 12: 35 5e-04 ***
## 13: 33 0.00081 ***
## 14: 25 0.00014 ***
## 15: 23 2.7e-05 ***
## 16: 19 0.00011 ***
## [1] 32.7
## [1] 6
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.0310 27 0.29 :(
## 2: 0.09375 0.0780 37 0.0098 **
## 3: 0.15625 -0.0015 40 0.94 :(
## 4: 0.21875 0.0490 43 0.37 :(
## 5: 0.28125 -0.0130 43 0.78 :(
## 6: 0.34375 -0.0480 38 0.3 :(
## 7: 0.40625 -0.0420 43 0.38 :(
## 8: 0.46875 -0.0230 41 0.66 :(
## 9: 0.53125 -0.0310 39 0.55 :(
## 10: 0.59375 -0.0460 39 0.35 :(
## 11: 0.65625 0.0020 43 0.92 :(
## 12: 0.71875 -0.0580 41 0.058 .
## 13: 0.78125 -0.0990 44 0.0041 **
## 14: 0.84375 -0.1800 42 6.1e-06 ***
## 15: 0.90625 -0.1900 34 3.5e-07 ***
## 16: 0.96875 -0.1700 32 8e-07 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 27 0.29 :(
## 2: 37 0.0098 **
## 3: 40 0.94 :(
## 4: 43 0.37 :(
## 5: 43 0.78 :(
## 6: 38 0.3 :(
## 7: 43 0.38 :(
## 8: 41 0.66 :(
## 9: 39 0.55 :(
## 10: 39 0.35 :(
## 11: 43 0.92 :(
## 12: 41 0.058 .
## 13: 44 0.0041 **
## 14: 42 6.1e-06 ***
## 15: 34 3.5e-07 ***
## 16: 32 8e-07 ***
## [1] 39.1
## [1] 4.69
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 4 NA
## 2: 0.09375 -0.094 9 0.53 :(
## 3: 0.15625 0.058 18 0.24 :(
## 4: 0.21875 0.110 20 0.059 .
## 5: 0.28125 0.150 22 0.064 .
## 6: 0.34375 0.130 21 0.04 *
## 7: 0.40625 -0.021 22 0.79 :(
## 8: 0.46875 -0.057 23 0.28 :(
## 9: 0.53125 -0.100 19 0.18 :(
## 10: 0.59375 -0.076 24 0.27 :(
## 11: 0.65625 -0.073 20 0.24 :(
## 12: 0.71875 -0.160 22 0.0063 **
## 13: 0.78125 -0.140 25 0.0015 **
## 14: 0.84375 -0.200 26 4.8e-05 ***
## 15: 0.90625 -0.160 26 8.5e-06 ***
## 16: 0.96875 -0.250 25 1.3e-05 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 9 0.53 :(
## 2: 18 0.24 :(
## 3: 20 0.059 .
## 4: 22 0.064 .
## 5: 21 0.04 *
## 6: 22 0.79 :(
## 7: 23 0.28 :(
## 8: 19 0.18 :(
## 9: 24 0.27 :(
## 10: 20 0.24 :(
## 11: 22 0.0063 **
## 12: 25 0.0015 **
## 13: 26 4.8e-05 ***
## 14: 26 8.5e-06 ***
## 15: 25 1.3e-05 ***
## [1] 21.5
## [1] 4.26## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
Motor task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 -0.0940 8 0.71 :(
## 3: 0.15625 -0.0990 29 0.021 *
## 4: 0.21875 -0.0760 41 0.042 *
## 5: 0.28125 -0.0540 48 0.2 :(
## 6: 0.34375 -0.0400 50 0.22 :(
## 7: 0.40625 -0.0015 50 0.9 :(
## 8: 0.46875 -0.0022 54 1 :(
## 9: 0.53125 0.0400 52 0.17 :(
## 10: 0.59375 0.0063 51 0.82 :(
## 11: 0.65625 0.0220 52 0.79 :(
## 12: 0.71875 -0.0580 53 0.064 .
## 13: 0.78125 -0.0790 46 0.015 *
## 14: 0.84375 -0.0940 29 0.077 .
## 15: 0.90625 -0.0760 13 0.0012 **
## 16: 0.96875 -0.1100 6 0.031 *
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 8 0.71 :(
## 2: 29 0.021 *
## 3: 41 0.042 *
## 4: 48 0.2 :(
## 5: 50 0.22 :(
## 6: 50 0.9 :(
## 7: 54 1 :(
## 8: 52 0.17 :(
## 9: 51 0.82 :(
## 10: 52 0.79 :(
## 11: 53 0.064 .
## 12: 46 0.015 *
## 13: 29 0.077 .
## 14: 13 0.0012 **
## 15: 6 0.031 *
## [1] 38.8
## [1] 17.3## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 -0.094 8 0.71 :(
## 3: 0.15625 -0.099 24 0.023 *
## 4: 0.21875 -0.066 25 0.067 .
## 5: 0.28125 -0.043 25 0.31 :(
## 6: 0.34375 -0.040 25 0.32 :(
## 7: 0.40625 0.040 24 0.4 :(
## 8: 0.46875 0.067 24 0.12 :(
## 9: 0.53125 0.110 23 0.021 *
## 10: 0.59375 0.120 22 0.043 *
## 11: 0.65625 0.029 22 0.52 :(
## 12: 0.71875 -0.040 21 0.094 .
## 13: 0.78125 -0.067 15 0.32 :(
## 14: 0.84375 NA 2 NA
## 15: 0.90625 NA 0 NA
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 8 0.71 :(
## 2: 24 0.023 *
## 3: 25 0.067 .
## 4: 25 0.31 :(
## 5: 25 0.32 :(
## 6: 24 0.4 :(
## 7: 24 0.12 :(
## 8: 23 0.021 *
## 9: 22 0.043 *
## 10: 22 0.52 :(
## 11: 21 0.094 .
## 12: 15 0.32 :(
## [1] 21.5
## [1] 5.07## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 4 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 NA 5 NA
## 4: 0.21875 -0.0045 16 0.41 :(
## 5: 0.28125 -0.0670 23 0.51 :(
## 6: 0.34375 -0.0580 24 0.3 :(
## 7: 0.40625 -0.0320 25 0.73 :(
## 8: 0.46875 -0.0400 25 0.5 :(
## 9: 0.53125 0.0220 25 0.69 :(
## 10: 0.59375 -0.0220 22 0.9 :(
## 11: 0.65625 0.0410 23 0.66 :(
## 12: 0.71875 0.0310 25 0.65 :(
## 13: 0.78125 -0.0670 25 0.13 :(
## 14: 0.84375 -0.0940 20 0.15 :(
## 15: 0.90625 NA 6 NA
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 16 0.41 :(
## 2: 23 0.51 :(
## 3: 24 0.3 :(
## 4: 25 0.73 :(
## 5: 25 0.5 :(
## 6: 25 0.69 :(
## 7: 22 0.9 :(
## 8: 23 0.66 :(
## 9: 25 0.65 :(
## 10: 25 0.13 :(
## 11: 20 0.15 :(
## [1] 23
## [1] 2.83## Warning: Removed 5 rows containing missing values (geom_point).## Warning: Removed 5 rows containing missing values (geom_errorbar).
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 NA 0 NA
## 4: 0.21875 NA 0 NA
## 5: 0.28125 NA 0 NA
## 6: 0.34375 NA 1 NA
## 7: 0.40625 NA 1 NA
## 8: 0.46875 -0.150 5 0.28 :(
## 9: 0.53125 -0.220 4 0.38 :(
## 10: 0.59375 -0.290 7 0.078 .
## 11: 0.65625 -0.130 7 0.35 :(
## 12: 0.71875 -0.260 7 0.047 *
## 13: 0.78125 -0.160 6 0.16 :(
## 14: 0.84375 -0.120 7 0.2 :(
## 15: 0.90625 -0.081 7 0.022 *
## 16: 0.96875 -0.110 6 0.031 *
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 5 0.28 :(
## 2: 4 0.38 :(
## 3: 7 0.078 .
## 4: 7 0.35 :(
## 5: 7 0.047 *
## 6: 6 0.16 :(
## 7: 7 0.2 :(
## 8: 7 0.022 *
## 9: 6 0.031 *
## [1] 6.22
## [1] 1.09## Warning: Removed 7 rows containing missing values (geom_point).## Warning: Removed 7 rows containing missing values (geom_errorbar).
Sensory task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.0310 32 0.034 *
## 2: 0.09375 -0.0065 48 0.64 :(
## 3: 0.15625 -0.0970 51 0.0069 **
## 4: 0.21875 -0.0760 47 0.0011 **
## 5: 0.28125 -0.0670 46 0.1 :(
## 6: 0.34375 -0.1300 41 0.063 .
## 7: 0.40625 -0.1200 44 0.053 .
## 8: 0.46875 -0.1100 42 0.036 *
## 9: 0.53125 -0.1700 34 0.0079 **
## 10: 0.59375 -0.2400 37 0.00062 ***
## 11: 0.65625 -0.1100 40 0.12 :(
## 12: 0.71875 -0.1700 46 0.00063 ***
## 13: 0.78125 -0.1700 42 0.0042 **
## 14: 0.84375 -0.1700 46 9e-06 ***
## 15: 0.90625 -0.1600 53 1.9e-10 ***
## 16: 0.96875 -0.1400 55 9.4e-11 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 32 0.034 *
## 2: 48 0.64 :(
## 3: 51 0.0069 **
## 4: 47 0.0011 **
## 5: 46 0.1 :(
## 6: 41 0.063 .
## 7: 44 0.053 .
## 8: 42 0.036 *
## 9: 34 0.0079 **
## 10: 37 0.00062 ***
## 11: 40 0.12 :(
## 12: 46 0.00063 ***
## 13: 42 0.0042 **
## 14: 46 9e-06 ***
## 15: 53 1.9e-10 ***
## 16: 55 9.4e-11 ***
## [1] 44
## [1] 6.4
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0044 18 1 :(
## 2: 0.09375 -0.0530 19 0.033 *
## 3: 0.15625 -0.1600 17 0.048 *
## 4: 0.21875 -0.1500 13 0.019 *
## 5: 0.28125 -0.1000 13 0.29 :(
## 6: 0.34375 -0.1300 13 0.024 *
## 7: 0.40625 -0.2600 14 0.008 **
## 8: 0.46875 -0.1100 16 0.22 :(
## 9: 0.53125 -0.2100 14 0.044 *
## 10: 0.59375 -0.4400 11 0.005 **
## 11: 0.65625 -0.1600 13 0.069 .
## 12: 0.71875 -0.1800 16 0.0065 **
## 13: 0.78125 -0.2800 13 0.03 *
## 14: 0.84375 -0.1700 15 0.011 *
## 15: 0.90625 -0.1400 18 0.00018 ***
## 16: 0.96875 -0.1500 19 0.00011 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 18 1 :(
## 2: 19 0.033 *
## 3: 17 0.048 *
## 4: 13 0.019 *
## 5: 13 0.29 :(
## 6: 13 0.024 *
## 7: 14 0.008 **
## 8: 16 0.22 :(
## 9: 14 0.044 *
## 10: 11 0.005 **
## 11: 13 0.069 .
## 12: 16 0.0065 **
## 13: 13 0.03 *
## 14: 15 0.011 *
## 15: 18 0.00018 ***
## 16: 19 0.00011 ***
## [1] 15.1
## [1] 2.5
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 14 NA
## 2: 0.09375 0.021 26 0.4 :(
## 3: 0.15625 -0.097 26 0.026 *
## 4: 0.21875 -0.110 25 0.0067 **
## 5: 0.28125 -0.100 24 0.21 :(
## 6: 0.34375 -0.058 20 0.61 :(
## 7: 0.40625 -0.085 22 0.45 :(
## 8: 0.46875 -0.110 20 0.16 :(
## 9: 0.53125 -0.150 16 0.15 :(
## 10: 0.59375 -0.170 21 0.14 :(
## 11: 0.65625 -0.160 21 0.55 :(
## 12: 0.71875 -0.076 22 0.026 *
## 13: 0.78125 -0.120 21 0.087 .
## 14: 0.84375 -0.170 24 0.0026 **
## 15: 0.90625 -0.160 26 8.2e-06 ***
## 16: 0.96875 -0.150 27 5.6e-06 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 26 0.4 :(
## 2: 26 0.026 *
## 3: 25 0.0067 **
## 4: 24 0.21 :(
## 5: 20 0.61 :(
## 6: 22 0.45 :(
## 7: 20 0.16 :(
## 8: 16 0.15 :(
## 9: 21 0.14 :(
## 10: 21 0.55 :(
## 11: 22 0.026 *
## 12: 21 0.087 .
## 13: 24 0.0026 **
## 14: 26 8.2e-06 ***
## 15: 27 5.6e-06 ***
## [1] 22.7
## [1] 3.03## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 3 NA
## 3: 0.15625 0.020 8 0.94 :(
## 4: 0.21875 0.013 9 0.81 :(
## 5: 0.28125 -0.013 9 1 :(
## 6: 0.34375 -0.078 8 0.72 :(
## 7: 0.40625 0.064 8 0.53 :(
## 8: 0.46875 -0.088 6 0.53 :(
## 9: 0.53125 -0.230 4 0.36 :(
## 10: 0.59375 -0.170 5 0.42 :(
## 11: 0.65625 0.022 6 0.83 :(
## 12: 0.71875 -0.130 8 0.62 :(
## 13: 0.78125 -0.077 8 0.53 :(
## 14: 0.84375 -0.120 7 0.075 .
## 15: 0.90625 -0.160 9 0.0086 **
## 16: 0.96875 -0.120 9 0.0091 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 8 0.94 :(
## 2: 9 0.81 :(
## 3: 9 1 :(
## 4: 8 0.72 :(
## 5: 8 0.53 :(
## 6: 6 0.53 :(
## 7: 4 0.36 :(
## 8: 5 0.42 :(
## 9: 6 0.83 :(
## 10: 8 0.62 :(
## 11: 8 0.53 :(
## 12: 7 0.075 .
## 13: 9 0.0086 **
## 14: 9 0.0091 **
## [1] 7.43
## [1] 1.6## Warning: Removed 2 rows containing missing values (geom_point).## Warning: Removed 2 rows containing missing values (geom_errorbar).
Logical task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0005 38 0.78 :(
## 2: 0.09375 0.0970 43 0.01 *
## 3: 0.15625 0.0940 48 0.04 *
## 4: 0.21875 0.1600 50 0.0046 **
## 5: 0.28125 0.1500 49 0.015 *
## 6: 0.34375 0.0850 41 0.08 .
## 7: 0.40625 0.0220 47 0.77 :(
## 8: 0.46875 -0.0400 47 0.64 :(
## 9: 0.53125 0.0160 45 0.73 :(
## 10: 0.59375 -0.0370 46 0.6 :(
## 11: 0.65625 -0.0490 42 0.32 :(
## 12: 0.71875 -0.1500 41 0.00057 ***
## 13: 0.78125 -0.1400 53 0.00026 ***
## 14: 0.84375 -0.2600 52 1.4e-08 ***
## 15: 0.90625 -0.2400 42 1.7e-08 ***
## 16: 0.96875 -0.3300 29 2.7e-06 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 38 0.78 :(
## 2: 43 0.01 *
## 3: 48 0.04 *
## 4: 50 0.0046 **
## 5: 49 0.015 *
## 6: 41 0.08 .
## 7: 47 0.77 :(
## 8: 47 0.64 :(
## 9: 45 0.73 :(
## 10: 46 0.6 :(
## 11: 42 0.32 :(
## 12: 41 0.00057 ***
## 13: 53 0.00026 ***
## 14: 52 1.4e-08 ***
## 15: 42 1.7e-08 ***
## 16: 29 2.7e-06 ***
## [1] 44.6
## [1] 5.93
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.012 16 0.51 :(
## 2: 0.09375 0.044 16 0.37 :(
## 3: 0.15625 0.058 15 0.8 :(
## 4: 0.21875 0.031 14 0.8 :(
## 5: 0.28125 -0.048 11 0.69 :(
## 6: 0.34375 -0.082 13 0.67 :(
## 7: 0.40625 -0.085 9 0.72 :(
## 8: 0.46875 -0.040 14 0.49 :(
## 9: 0.53125 0.040 14 0.45 :(
## 10: 0.59375 -0.170 13 0.18 :(
## 11: 0.65625 -0.160 11 0.12 :(
## 12: 0.71875 -0.280 11 0.014 *
## 13: 0.78125 -0.280 15 0.033 *
## 14: 0.84375 -0.390 12 0.011 *
## 15: 0.90625 -0.410 7 0.022 *
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 16 0.51 :(
## 2: 16 0.37 :(
## 3: 15 0.8 :(
## 4: 14 0.8 :(
## 5: 11 0.69 :(
## 6: 13 0.67 :(
## 7: 9 0.72 :(
## 8: 14 0.49 :(
## 9: 14 0.45 :(
## 10: 13 0.18 :(
## 11: 11 0.12 :(
## 12: 11 0.014 *
## 13: 15 0.033 *
## 14: 12 0.011 *
## 15: 7 0.022 *
## [1] 12.7
## [1] 2.58## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.016 18 0.75 :(
## 2: 0.09375 0.150 21 0.0031 **
## 3: 0.15625 0.130 22 0.048 *
## 4: 0.21875 0.190 21 0.016 *
## 5: 0.28125 0.110 21 0.13 :(
## 6: 0.34375 0.085 14 0.23 :(
## 7: 0.40625 0.022 20 0.42 :(
## 8: 0.46875 0.140 17 0.32 :(
## 9: 0.53125 0.040 16 0.78 :(
## 10: 0.59375 -0.022 17 0.96 :(
## 11: 0.65625 -0.013 18 0.79 :(
## 12: 0.71875 -0.150 17 0.2 :(
## 13: 0.78125 -0.140 20 0.12 :(
## 14: 0.84375 -0.200 21 0.00015 ***
## 15: 0.90625 -0.200 16 0.00048 ***
## 16: 0.96875 -0.400 10 0.0057 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 18 0.75 :(
## 2: 21 0.0031 **
## 3: 22 0.048 *
## 4: 21 0.016 *
## 5: 21 0.13 :(
## 6: 14 0.23 :(
## 7: 20 0.42 :(
## 8: 17 0.32 :(
## 9: 16 0.78 :(
## 10: 17 0.96 :(
## 11: 18 0.79 :(
## 12: 17 0.2 :(
## 13: 20 0.12 :(
## 14: 21 0.00015 ***
## 15: 16 0.00048 ***
## 16: 10 0.0057 **
## [1] 18.1
## [1] 3.17
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 4 NA
## 2: 0.09375 NA 6 NA
## 3: 0.15625 0.082 11 0.26 :(
## 4: 0.21875 0.210 15 0.056 .
## 5: 0.28125 0.220 17 0.017 *
## 6: 0.34375 0.230 14 0.032 *
## 7: 0.40625 0.022 18 0.97 :(
## 8: 0.46875 -0.040 16 0.34 :(
## 9: 0.53125 -0.031 15 0.93 :(
## 10: 0.59375 0.085 16 0.66 :(
## 11: 0.65625 0.022 13 0.83 :(
## 12: 0.71875 -0.150 13 0.025 *
## 13: 0.78125 -0.120 18 0.011 *
## 14: 0.84375 -0.220 19 0.0013 **
## 15: 0.90625 -0.190 19 0.00014 ***
## 16: 0.96875 -0.300 19 0.00014 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 11 0.26 :(
## 2: 15 0.056 .
## 3: 17 0.017 *
## 4: 14 0.032 *
## 5: 18 0.97 :(
## 6: 16 0.34 :(
## 7: 15 0.93 :(
## 8: 16 0.66 :(
## 9: 13 0.83 :(
## 10: 13 0.025 *
## 11: 18 0.011 *
## 12: 19 0.0013 **
## 13: 19 0.00014 ***
## 14: 19 0.00014 ***
## [1] 15.9
## [1] 2.56## Warning: Removed 2 rows containing missing values (geom_point).## Warning: Removed 2 rows containing missing values (geom_errorbar).
Influence of Playtime on Subjective Difficulty Error
For all groups, motor, sensitive and logical
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.85425 -0.20543 0.02783 0.20243 0.70750
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.006664 0.018685 -0.357 0.721400
## timeNorm 0.016339 0.020930 0.781 0.435103
## obj.diff -0.094710 0.028659 -3.305 0.000969 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07620175)
##
## Null deviance: 143.99 on 1880 degrees of freedom
## Residual deviance: 143.11 on 1878 degrees of freedom
## AIC: 500.65
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.78155 -0.13780 -0.01151 0.12280 0.83005
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.006795 0.014205 -0.478 0.632
## timeNorm 0.011968 0.020729 0.577 0.564
## obj.diff -0.205459 0.018132 -11.331 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.0706054)
##
## Null deviance: 137.08 on 1814 degrees of freedom
## Residual deviance: 127.94 on 1812 degrees of freedom
## AIC: 344.82
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.71153 -0.24157 0.00719 0.24129 0.65825
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.17431 0.01800 9.684 <2e-16 ***
## timeNorm 0.02107 0.02417 0.872 0.384
## obj.diff -0.46661 0.02329 -20.037 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1007759)
##
## Null deviance: 230.47 on 1880 degrees of freedom
## Residual deviance: 189.26 on 1878 degrees of freedom
## AIC: 1026.4
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3918129 0.4457102 -0.05543814 342 0.0017 **
## 2: 4.5 0.4954052 0.5624859 -0.05783222 171 0.0052 **
## 3: 7.5 0.4928989 0.5357049 -0.03693287 171 0.077 .
## 4: 10.5 0.5071011 0.5362058 -0.02578583 171 0.23 :(
## 5: 13.5 0.4519632 0.5133937 -0.05576376 171 0.0061 **
## 6: 16.5 0.4995823 0.5320036 -0.01539779 171 0.46 :(
## 7: 19.5 0.4803676 0.5358363 -0.04608428 171 0.025 *
## 8: 22.5 0.4527987 0.4961373 -0.03638516 171 0.091 .
## 9: 25.5 0.4536341 0.4868060 -0.02527202 171 0.27 :(
## 10: 28.5 0.4243943 0.4657574 -0.03934980 171 0.074 .
## time error.diff shapes
## 1: 1.5 -0.05543814 24
## 2: 4.5 -0.05783222 24
## 3: 7.5 -0.03693287 16
## 4: 10.5 -0.02578583 16
## 5: 13.5 -0.05576376 24
## 6: 16.5 -0.01539779 16
## 7: 19.5 -0.04608428 24
## 8: 22.5 -0.03638516 16
## 9: 25.5 -0.02527202 16
## 10: 28.5 -0.03934980 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.2761905 0.3121174 -0.08025653 330 2.2e-05 ***
## 2: 4.5 0.5194805 0.6623901 -0.12246157 165 6.5e-13 ***
## 3: 7.5 0.4259740 0.5756691 -0.12748326 165 6.7e-13 ***
## 4: 10.5 0.4658009 0.6169890 -0.12563962 165 5.6e-14 ***
## 5: 13.5 0.4251082 0.5882784 -0.13475627 165 4.2e-16 ***
## 6: 16.5 0.4025974 0.5480044 -0.12850300 165 1.9e-12 ***
## 7: 19.5 0.4666667 0.5706900 -0.09391859 165 2.3e-08 ***
## 8: 22.5 0.4311688 0.5568448 -0.12173493 165 1.6e-10 ***
## 9: 25.5 0.4891775 0.5635905 -0.08515151 165 7.1e-08 ***
## 10: 28.5 0.4649351 0.5525507 -0.08873994 165 1.2e-07 ***
## time error.diff shapes
## 1: 1.5 -0.08025653 24
## 2: 4.5 -0.12246157 24
## 3: 7.5 -0.12748326 24
## 4: 10.5 -0.12563962 24
## 5: 13.5 -0.13475627 24
## 6: 16.5 -0.12850300 24
## 7: 19.5 -0.09391859 24
## 8: 22.5 -0.12173493 24
## 9: 25.5 -0.08515151 24
## 10: 28.5 -0.08873994 24
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3483709 0.3515160 -0.0257788254 342 0.26 :(
## 2: 4.5 0.5037594 0.6513076 -0.1432600132 171 4.6e-08 ***
## 3: 7.5 0.5037594 0.5682979 -0.0702473202 171 0.0057 **
## 4: 10.5 0.4970760 0.5388474 -0.0530333212 171 0.04 *
## 5: 13.5 0.4761905 0.5225795 -0.0457087630 171 0.099 .
## 6: 16.5 0.4820384 0.5042410 -0.0325739632 171 0.21 :(
## 7: 19.5 0.4185464 0.4415088 -0.0319575055 171 0.25 :(
## 8: 22.5 0.3918129 0.4078173 -0.0213488721 171 0.43 :(
## 9: 25.5 0.3851295 0.3856125 -0.0035008941 171 0.9 :(
## 10: 28.5 0.3792815 0.3513216 -0.0006985616 171 0.98 :(
## time error.diff shapes
## 1: 1.5 -0.0257788254 16
## 2: 4.5 -0.1432600132 24
## 3: 7.5 -0.0702473202 24
## 4: 10.5 -0.0530333212 24
## 5: 13.5 -0.0457087630 16
## 6: 16.5 -0.0325739632 16
## 7: 19.5 -0.0319575055 16
## 8: 22.5 -0.0213488721 16
## 9: 25.5 -0.0035008941 16
## 10: 28.5 -0.0006985616 16
For all taks, per group
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group ==
## "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7283 -0.2728 0.1294 0.2016 0.5850
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.21505 0.02876 7.477 1.5e-13 ***
## timeNorm 0.01392 0.03098 0.449 0.653
## obj.diff -0.48637 0.03430 -14.179 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1024864)
##
## Null deviance: 138.68 on 1154 degrees of freedom
## Residual deviance: 118.06 on 1152 degrees of freedom
## AIC: 651.62
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group ==
## "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.79280 -0.20411 0.04097 0.20291 0.72423
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06731 0.01468 4.585 4.76e-06 ***
## timeNorm 0.04344 0.01935 2.245 0.0248 *
## obj.diff -0.27646 0.02013 -13.731 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.08439343)
##
## Null deviance: 221.99 on 2441 degrees of freedom
## Residual deviance: 205.84 on 2439 degrees of freedom
## AIC: 897.82
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group ==
## "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.73813 -0.18312 -0.06486 0.18831 0.79768
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03629 0.01418 2.558 0.0106 *
## timeNorm 0.01309 0.02010 0.651 0.5148
## obj.diff -0.23787 0.02195 -10.836 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07396167)
##
## Null deviance: 154.93 on 1979 degrees of freedom
## Residual deviance: 146.22 on 1977 degrees of freedom
## AIC: 467.66
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4795918 0.5376740 -0.06317999 210 0.022 *
## 2: 4.5 0.6340136 0.8056827 -0.13614192 105 4.3e-08 ***
## 3: 7.5 0.6040816 0.7450231 -0.12356755 105 1.6e-05 ***
## 4: 10.5 0.6108844 0.7038259 -0.09260169 105 0.003 **
## 5: 13.5 0.5931973 0.7173646 -0.10966239 105 7e-05 ***
## 6: 16.5 0.5986395 0.7274973 -0.10609599 105 9e-05 ***
## 7: 19.5 0.5687075 0.6685055 -0.08310054 105 0.012 *
## 8: 22.5 0.5687075 0.6739166 -0.08713138 105 0.0027 **
## 9: 25.5 0.5238095 0.6393948 -0.10457762 105 0.00057 ***
## 10: 28.5 0.5523810 0.5934189 -0.04289068 105 0.073 .
## time error.diff shapes
## 1: 1.5 -0.06317999 24
## 2: 4.5 -0.13614192 24
## 3: 7.5 -0.12356755 24
## 4: 10.5 -0.09260169 24
## 5: 13.5 -0.10966239 24
## 6: 16.5 -0.10609599 24
## 7: 19.5 -0.08310054 24
## 8: 22.5 -0.08713138 24
## 9: 25.5 -0.10457762 24
## 10: 28.5 -0.04289068 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3407336 0.3611307 -0.04003289 444 0.027 *
## 2: 4.5 0.5276705 0.6559888 -0.11981339 222 2.2e-10 ***
## 3: 7.5 0.4800515 0.5451680 -0.06872960 222 0.00013 ***
## 4: 10.5 0.5038610 0.5839184 -0.07900619 222 4.3e-05 ***
## 5: 13.5 0.4781210 0.5578799 -0.07753452 222 9.1e-05 ***
## 6: 16.5 0.4768340 0.5217832 -0.04662507 222 0.023 *
## 7: 19.5 0.4961390 0.5390272 -0.04371726 222 0.013 *
## 8: 22.5 0.4118404 0.4689708 -0.06669797 222 0.00091 ***
## 9: 25.5 0.4671815 0.4777792 -0.02515232 222 0.25 :(
## 10: 28.5 0.4665380 0.4859167 -0.03352356 222 0.093 .
## time error.diff shapes
## 1: 1.5 -0.04003289 24
## 2: 4.5 -0.11981339 24
## 3: 7.5 -0.06872960 24
## 4: 10.5 -0.07900619 24
## 5: 13.5 -0.07753452 24
## 6: 16.5 -0.04662507 24
## 7: 19.5 -0.04371726 24
## 8: 22.5 -0.06669797 24
## 9: 25.5 -0.02515232 16
## 10: 28.5 -0.03352356 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.2563492 0.2844347 -0.05793583 360 3e-04 ***
## 2: 4.5 0.4047619 0.4812604 -0.08091450 180 0.00048 ***
## 3: 7.5 0.3928571 0.4695287 -0.07654093 180 0.00061 ***
## 4: 10.5 0.4031746 0.4561427 -0.06203485 180 0.0018 **
## 5: 13.5 0.3357143 0.4169152 -0.08655490 180 2.7e-05 ***
## 6: 16.5 0.3642857 0.4188636 -0.05961419 180 0.004 **
## 7: 19.5 0.3380952 0.3968486 -0.06239372 180 0.0014 **
## 8: 22.5 0.3579365 0.3976826 -0.04670165 180 0.029 *
## 9: 25.5 0.3634921 0.3831808 -0.02729630 180 0.21 :(
## 10: 28.5 0.2920635 0.3372715 -0.05901936 180 0.0027 **
## time error.diff shapes
## 1: 1.5 -0.05793583 24
## 2: 4.5 -0.08091450 24
## 3: 7.5 -0.07654093 24
## 4: 10.5 -0.06203485 24
## 5: 13.5 -0.08655490 24
## 6: 16.5 -0.05961419 24
## 7: 19.5 -0.06239372 24
## 8: 22.5 -0.04670165 24
## 9: 25.5 -0.02729630 16
## 10: 28.5 -0.05901936 24
Per group, motor task
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group ==
## "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.78307 -0.20030 0.09965 0.20287 0.56643
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.22999 0.11949 -1.925 0.0555 .
## timeNorm -0.03598 0.06998 -0.514 0.6077
## obj.diff 0.08283 0.15050 0.550 0.5826
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1038657)
##
## Null deviance: 23.736 on 230 degrees of freedom
## Residual deviance: 23.681 on 228 degrees of freedom
## AIC: 137.39
##
## Number of Fisher Scoring iterations: 2## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): cannot compute exact confidence interval with ties## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5374150 0.7170177 -0.17337940 42 0.011 *
## 2: 4.5 0.6122449 0.7946114 -0.14180958 21 0.0063 **
## 3: 7.5 0.6326531 0.7649789 -0.07966557 21 0.038 *
## 4: 10.5 0.6394558 0.7869246 -0.09982316 21 0.009 **
## 5: 13.5 0.6190476 0.8120284 -0.09702938 21 0.013 *
## 6: 16.5 0.5102041 0.7887369 -0.26023913 21 0.0049 **
## 7: 19.5 0.5442177 0.7250289 -0.16961596 21 0.05 .
## 8: 22.5 0.6462585 0.7637626 -0.03896849 21 0.49 :(
## 9: 25.5 0.5578231 0.8157609 -0.26561302 21 0.00072 ***
## 10: 28.5 0.5986395 0.7674702 -0.09317669 21 0.06 .
## time error.diff shapes
## 1: 1.5 -0.17337940 24
## 2: 4.5 -0.14180958 24
## 3: 7.5 -0.07966557 24
## 4: 10.5 -0.09982316 24
## 5: 13.5 -0.09702938 24
## 6: 16.5 -0.26023913 24
## 7: 19.5 -0.16961596 16
## 8: 22.5 -0.03896849 16
## 9: 25.5 -0.26561302 24
## 10: 28.5 -0.09317669 16## Warning: Removed 2 rows containing missing values (geom_errorbar).
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group ==
## "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.79768 -0.22927 0.04496 0.19205 0.68904
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04646 0.03146 -1.477 0.140
## timeNorm 0.01656 0.03145 0.527 0.599
## obj.diff -0.01154 0.04892 -0.236 0.814
##
## (Dispersion parameter for gaussian family taken to be 0.07542452)
##
## Null deviance: 62.024 on 824 degrees of freedom
## Residual deviance: 61.999 on 822 degrees of freedom
## AIC: 213.93
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4219048 0.4703926 -0.04882302 150 0.077 .
## 2: 4.5 0.5314286 0.6181213 -0.07545117 75 0.02 *
## 3: 7.5 0.5028571 0.5405554 -0.02915190 75 0.35 :(
## 4: 10.5 0.5333333 0.5682867 -0.03243828 75 0.34 :(
## 5: 13.5 0.5200000 0.5516441 -0.02674953 75 0.43 :(
## 6: 16.5 0.5428571 0.5685882 -0.02122707 75 0.62 :(
## 7: 19.5 0.5447619 0.5794923 -0.02965771 75 0.39 :(
## 8: 22.5 0.4380952 0.5231952 -0.09195474 75 0.015 *
## 9: 25.5 0.4819048 0.5079792 -0.03043348 75 0.41 :(
## 10: 28.5 0.4819048 0.5148979 -0.03878182 75 0.31 :(
## time error.diff shapes
## 1: 1.5 -0.04882302 16
## 2: 4.5 -0.07545117 24
## 3: 7.5 -0.02915190 16
## 4: 10.5 -0.03243828 16
## 5: 13.5 -0.02674953 16
## 6: 16.5 -0.02122707 16
## 7: 19.5 -0.02965771 16
## 8: 22.5 -0.09195474 24
## 9: 25.5 -0.03043348 16
## 10: 28.5 -0.03878182 16
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group ==
## "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.80335 -0.18155 -0.01888 0.19399 0.72918
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.06141 0.02493 -2.463 0.0140 *
## timeNorm 0.03220 0.02896 1.112 0.2664
## obj.diff 0.08952 0.04587 1.952 0.0513 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06387276)
##
## Null deviance: 52.815 on 824 degrees of freedom
## Residual deviance: 52.503 on 822 degrees of freedom
## AIC: 76.782
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3209524 0.3450617 -0.03697026 150 0.13 :(
## 2: 4.5 0.4266667 0.4418554 -0.01357897 75 0.61 :(
## 3: 7.5 0.4438095 0.4666577 -0.02092597 75 0.44 :(
## 4: 10.5 0.4438095 0.4339237 0.01170254 75 0.74 :(
## 5: 13.5 0.3371429 0.3915256 -0.05898709 75 0.041 *
## 6: 16.5 0.4533333 0.4235337 0.02549517 75 0.27 :(
## 7: 19.5 0.3980952 0.4392064 -0.03752952 75 0.23 :(
## 8: 22.5 0.4133333 0.3941442 0.01243771 75 0.56 :(
## 9: 25.5 0.3961905 0.3735255 0.02575106 75 0.45 :(
## 10: 28.5 0.3180952 0.3321373 -0.01719639 75 0.56 :(
## time error.diff shapes
## 1: 1.5 -0.03697026 16
## 2: 4.5 -0.01357897 16
## 3: 7.5 -0.02092597 16
## 4: 10.5 0.01170254 16
## 5: 13.5 -0.05898709 24
## 6: 16.5 0.02549517 16
## 7: 19.5 -0.03752952 16
## 8: 22.5 0.01243771 16
## 9: 25.5 0.02575106 16
## 10: 28.5 -0.01719639 16
Per group, sensory task
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group ==
## "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.78743 -0.20369 0.06208 0.11509 0.68210
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.09528 0.04255 2.239 0.0259 *
## timeNorm 0.02131 0.05414 0.394 0.6942
## obj.diff -0.30196 0.05180 -5.830 1.46e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07877575)
##
## Null deviance: 25.854 on 296 degrees of freedom
## Residual deviance: 23.160 on 294 degrees of freedom
## AIC: 93.113
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4232804 0.4183202 -0.01635641 54 0.73 :(
## 2: 4.5 0.6613757 0.7447332 -0.09966346 27 0.052 .
## 3: 7.5 0.5502646 0.7363338 -0.15936527 27 0.0042 **
## 4: 10.5 0.5661376 0.7314530 -0.14016274 27 0.007 **
## 5: 13.5 0.5767196 0.7392145 -0.12926885 27 0.00092 ***
## 6: 16.5 0.5555556 0.6825762 -0.12651064 27 0.0065 **
## 7: 19.5 0.6349206 0.7056857 -0.09010470 27 0.26 :(
## 8: 22.5 0.6507937 0.7325326 -0.12078171 27 0.03 *
## 9: 25.5 0.5132275 0.6521805 -0.13254820 27 0.00018 ***
## 10: 28.5 0.6031746 0.6002956 -0.03415719 27 0.51 :(
## time error.diff shapes
## 1: 1.5 -0.01635641 16
## 2: 4.5 -0.09966346 16
## 3: 7.5 -0.15936527 24
## 4: 10.5 -0.14016274 24
## 5: 13.5 -0.12926885 24
## 6: 16.5 -0.12651064 24
## 7: 19.5 -0.09010470 16
## 8: 22.5 -0.12078171 24
## 9: 25.5 -0.13254820 24
## 10: 28.5 -0.03415719 16
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group ==
## "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.79017 -0.14265 0.02575 0.12081 0.79367
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0007053 0.0208951 -0.034 0.973
## timeNorm 0.0108023 0.0298646 0.362 0.718
## obj.diff -0.2026250 0.0265323 -7.637 5.75e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07217403)
##
## Null deviance: 68.332 on 890 degrees of freedom
## Residual deviance: 64.091 on 888 degrees of freedom
## AIC: 191.39
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.2980600 0.3201704 -0.07870225 162 0.05 .
## 2: 4.5 0.5608466 0.7228117 -0.12948975 81 5.4e-08 ***
## 3: 7.5 0.4338624 0.5731369 -0.12792864 81 2.7e-06 ***
## 4: 10.5 0.4779541 0.6353644 -0.12983440 81 4.6e-07 ***
## 5: 13.5 0.4391534 0.6134256 -0.14621999 81 1.8e-09 ***
## 6: 16.5 0.4179894 0.5473494 -0.12095073 81 2.6e-05 ***
## 7: 19.5 0.4973545 0.5800608 -0.08629896 81 0.00044 ***
## 8: 22.5 0.3932981 0.5224855 -0.13022568 81 3.7e-05 ***
## 9: 25.5 0.5167549 0.5725870 -0.07754871 81 0.00026 ***
## 10: 28.5 0.4902998 0.5806617 -0.08946090 81 5.3e-06 ***
## time error.diff shapes
## 1: 1.5 -0.07870225 24
## 2: 4.5 -0.12948975 24
## 3: 7.5 -0.12792864 24
## 4: 10.5 -0.12983440 24
## 5: 13.5 -0.14621999 24
## 6: 16.5 -0.12095073 24
## 7: 19.5 -0.08629896 24
## 8: 22.5 -0.13022568 24
## 9: 25.5 -0.07754871 24
## 10: 28.5 -0.08946090 24
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group ==
## "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.6916 -0.1174 -0.0147 0.1402 0.8603
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.03694 0.02141 -1.725 0.085 .
## timeNorm 0.01685 0.03339 0.505 0.614
## obj.diff -0.21183 0.02890 -7.329 7.21e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06282483)
##
## Null deviance: 42.611 on 626 degrees of freedom
## Residual deviance: 39.203 on 624 degrees of freedom
## AIC: 49.179
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.1754386 0.2503670 -0.08390482 114 1.7e-07 ***
## 2: 4.5 0.3934837 0.5375232 -0.12642807 57 7.7e-06 ***
## 3: 7.5 0.3558897 0.5031632 -0.10772684 57 7.4e-07 ***
## 4: 10.5 0.4010025 0.5366569 -0.10986449 57 3.5e-07 ***
## 5: 13.5 0.3333333 0.4810470 -0.12968997 57 5.7e-06 ***
## 6: 16.5 0.3082707 0.4851906 -0.14622864 57 1.6e-07 ***
## 7: 19.5 0.3433584 0.4934283 -0.11261380 57 4.9e-06 ***
## 8: 22.5 0.3809524 0.5224507 -0.12657265 57 8.3e-06 ***
## 9: 25.5 0.4385965 0.5088421 -0.07721547 57 0.0098 **
## 10: 28.5 0.3634085 0.4899874 -0.10235022 57 0.00052 ***
## time error.diff shapes
## 1: 1.5 -0.08390482 24
## 2: 4.5 -0.12642807 24
## 3: 7.5 -0.10772684 24
## 4: 10.5 -0.10986449 24
## 5: 13.5 -0.12968997 24
## 6: 16.5 -0.14622864 24
## 7: 19.5 -0.11261380 24
## 8: 22.5 -0.12657265 24
## 9: 25.5 -0.07721547 24
## 10: 28.5 -0.10235022 24
Per group, logical task
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group ==
## "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.6899 -0.3042 0.1314 0.2349 0.4789
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.327848 0.039949 8.207 1.3e-15 ***
## timeNorm -0.004351 0.043288 -0.101 0.92
## obj.diff -0.633947 0.046829 -13.537 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1078885)
##
## Null deviance: 87.210 on 626 degrees of freedom
## Residual deviance: 67.322 on 624 degrees of freedom
## AIC: 388.23
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4849624 0.5281359 -0.04157421 114 0.27 :(
## 2: 4.5 0.6290727 0.8386323 -0.19425581 57 1.2e-05 ***
## 3: 7.5 0.6190476 0.7417870 -0.11473387 57 0.011 *
## 4: 10.5 0.6215539 0.6601240 -0.04393235 57 0.4 :(
## 5: 13.5 0.5914787 0.6721386 -0.09528694 57 0.08 .
## 6: 16.5 0.6516291 0.7262138 -0.06587885 57 0.074 .
## 7: 19.5 0.5463659 0.6300694 -0.07167203 57 0.18 :(
## 8: 22.5 0.5012531 0.6130500 -0.08720494 57 0.04 *
## 9: 25.5 0.5162907 0.5683615 -0.05218842 57 0.43 :(
## 10: 28.5 0.5112782 0.5260374 -0.02478075 57 0.47 :(
## time error.diff shapes
## 1: 1.5 -0.04157421 16
## 2: 4.5 -0.19425581 24
## 3: 7.5 -0.11473387 24
## 4: 10.5 -0.04393235 16
## 5: 13.5 -0.09528694 16
## 6: 16.5 -0.06587885 16
## 7: 19.5 -0.07167203 16
## 8: 22.5 -0.08720494 24
## 9: 25.5 -0.05218842 16
## 10: 28.5 -0.02478075 16
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group ==
## "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.70616 -0.25599 0.03072 0.24287 0.63940
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.16277 0.02810 5.793 1.03e-08 ***
## timeNorm 0.08214 0.03854 2.132 0.0334 *
## obj.diff -0.46349 0.03912 -11.849 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.09911969)
##
## Null deviance: 86.496 on 725 degrees of freedom
## Residual deviance: 71.664 on 723 degrees of freedom
## AIC: 387.2
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3008658 0.2872390 -0.008624106 132 0.87 :(
## 2: 4.5 0.4826840 0.6170103 -0.137094482 66 0.001 **
## 3: 7.5 0.5108225 0.5160840 -0.023124603 66 0.57 :(
## 4: 10.5 0.5021645 0.5385434 -0.046324082 66 0.32 :(
## 5: 13.5 0.4783550 0.4967963 -0.003628038 66 0.93 :(
## 6: 16.5 0.4740260 0.4372192 0.024297695 66 0.57 :(
## 7: 19.5 0.4393939 0.4426848 -0.025355834 66 0.65 :(
## 8: 22.5 0.4047619 0.3416750 0.045008506 66 0.24 :(
## 9: 25.5 0.3896104 0.3271059 0.067289348 66 0.15 :(
## 10: 28.5 0.4199134 0.3367055 0.068892304 66 0.12 :(
## time error.diff shapes
## 1: 1.5 -0.008624106 16
## 2: 4.5 -0.137094482 24
## 3: 7.5 -0.023124603 16
## 4: 10.5 -0.046324082 16
## 5: 13.5 -0.003628038 16
## 6: 16.5 0.024297695 16
## 7: 19.5 -0.025355834 16
## 8: 22.5 0.045008506 16
## 9: 25.5 0.067289348 16
## 10: 28.5 0.068892304 16
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group ==
## "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5917 -0.1870 -0.1249 0.2308 0.7379
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.15007 0.02964 5.063 5.72e-07 ***
## timeNorm -0.05160 0.04248 -1.215 0.225
## obj.diff -0.49282 0.04761 -10.351 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.08574876)
##
## Null deviance: 54.225 on 527 degrees of freedom
## Residual deviance: 45.018 on 525 degrees of freedom
## AIC: 206.45
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.2514881 0.2301605 -0.04128074 96 0.29 :(
## 2: 4.5 0.3839286 0.4760184 -0.09387526 48 0.084 .
## 3: 7.5 0.3571429 0.4340736 -0.07703553 48 0.12 :(
## 4: 10.5 0.3422619 0.3952495 -0.06600414 48 0.05 .
## 5: 13.5 0.3363095 0.3804299 -0.04404735 48 0.38 :(
## 6: 16.5 0.2916667 0.3328032 -0.05676110 48 0.24 :(
## 7: 19.5 0.2380952 0.2159761 -0.01912010 48 0.8 :(
## 8: 22.5 0.2440476 0.2550491 -0.02616089 48 0.62 :(
## 9: 25.5 0.2232143 0.2490444 -0.04238278 48 0.27 :(
## 10: 28.5 0.1666667 0.1639436 -0.03508540 48 0.12 :(
## time error.diff shapes
## 1: 1.5 -0.04128074 16
## 2: 4.5 -0.09387526 16
## 3: 7.5 -0.07703553 16
## 4: 10.5 -0.06600414 16
## 5: 13.5 -0.04404735 16
## 6: 16.5 -0.05676110 16
## 7: 19.5 -0.01912010 16
## 8: 22.5 -0.02616089 16
## 9: 25.5 -0.04238278 16
## 10: 28.5 -0.03508540 16
Link between d(t-1) and error
##
## Call:
## glm(formula = error.subj.diff.mise ~ est.confidence.norm, data = DTM)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.88650 -0.19474 0.02121 0.20015 0.73863
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.02572 0.01258 -2.044 0.0411 *
## est.confidence.norm -0.04321 0.02231 -1.937 0.0529 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07647613)
##
## Null deviance: 143.99 on 1880 degrees of freedom
## Residual deviance: 143.70 on 1879 degrees of freedom
## AIC: 506.41
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ est.confidence.norm, data = DTS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.87482 -0.09574 0.00360 0.07584 0.92532
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.10317 0.01295 -7.968 2.81e-15 ***
## est.confidence.norm -0.01433 0.02181 -0.657 0.511
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07559423)
##
## Null deviance: 137.08 on 1814 degrees of freedom
## Residual deviance: 137.05 on 1813 degrees of freedom
## AIC: 467.73
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ est.confidence.norm, data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.96055 -0.18551 -0.02757 0.20982 0.87052
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.031481 0.016416 -1.918 0.0553 .
## est.confidence.norm 0.001105 0.028651 0.039 0.9692
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1226532)
##
## Null deviance: 230.47 on 1880 degrees of freedom
## Residual deviance: 230.47 on 1879 degrees of freedom
## AIC: 1395
##
## Number of Fisher Scoring iterations: 2## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTAll
##
## REML criterion at convergence: 1633.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7925 -0.5834 -0.0572 0.5586 4.2448
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01823 0.1350
## Residual 0.07577 0.2753
## Number of obs: 5577, groups: IDjoueur, 58
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.04362 0.01956 77.00000 -2.230 0.0286 *
## est.confidence.norm -0.03058 0.01486 5560.00000 -2.058 0.0397 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.378
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTM
##
## REML criterion at convergence: -162
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5725 -0.6690 0.0186 0.6541 3.3451
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.02822 0.1680
## Residual 0.04882 0.2209
## Number of obs: 1881, groups: IDjoueur, 57
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.03875 0.02664 97.80000 -1.455 0.149
## est.confidence.norm -0.01640 0.02826 1731.70000 -0.580 0.562
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.516
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTS
##
## REML criterion at convergence: 315.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0734 -0.4939 0.0180 0.4206 3.9205
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01030 0.1015
## Residual 0.06558 0.2561
## Number of obs: 1815, groups: IDjoueur, 55
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.11702 0.02156 146.20000 -5.428 2.31e-07 ***
## est.confidence.norm 0.01259 0.03019 742.10000 0.417 0.677
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.721
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTL
##
## REML criterion at convergence: 998.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5425 -0.6144 -0.0593 0.5775 3.2918
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.03255 0.1804
## Residual 0.09176 0.3029
## Number of obs: 1881, groups: IDjoueur, 57
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.08898 0.03103 118.30000 -2.867 0.00490 **
## est.confidence.norm 0.11638 0.03715 1557.40000 3.133 0.00176 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.597